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Found 6 papers, 2 papers with code
Self-supervised Heterogeneous Graph Variational Autoencoders
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
Resist Label Noise with PGM for Graph Neural Networks
To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels.
Prompt Tuning for Multi-View Graph Contrastive Learning
Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks.
Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
Context-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session.
Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
In addition, we propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples to further improve node representation.
